scholarly journals Forecasting of minimum temperature over Gangtok

MAUSAM ◽  
2022 ◽  
Vol 46 (1) ◽  
pp. 63-68
Author(s):  
S.D. ATTRI ◽  
A.B. PANDYA ◽  
D.P. DUBEY
Keyword(s):  
Regression Model ◽  
Minimum Temperature ◽  
Regression Models ◽  
Point Cloud ◽  
Cloud Amount ◽  
Dew Point ◽  
Maximum Temperature ◽  
Cold Wave ◽  
Percentage Frequency

A study has been conducted to assess day-to-day changes, departure and persistence of minimum temperature and the frequency of cold wave and severe cold wave over Gangtok for five winter months i.e., November-March for the years 1969 to 1992. Regression models have also been formulated to forecast minimum temperature with the knowledge of dew point, cloud amount, maximum temperature and minimum temperature recorded on previous day. In case of changes, ‘little change’ and ‘no change’ constitute about four-fifth of total changes. The cases of nearly normal were found maximum when departure of minimum temperature from normal was considered. Frequency of cold wave and severe cold wave has been recorded more in January and February respectively. It has been observed that there is a gradual fall in the percentage frequency with the increase in the magnitude of variation. Regression model gives good results from November to February.   

scholarly journals Correlation Between Local Air Temperature and the COVID-19 Pandemic in Hubei, China

2021 ◽  
Vol 8 ◽  
Author(s):  
Cheng-yi Hu ◽  
Lu-shan Xiao ◽  
Hong-bo Zhu ◽  
Hong Zhu ◽  
Li Liu
Keyword(s):  
Air Temperature ◽  
Minimum Temperature ◽  
Regression Models ◽  
Hubei Province ◽  
Daily Temperature ◽  
Maximum Temperature ◽  
Influence Of Temperature ◽  
Influence Of Temperature On ◽  
Almost All

Objective: To clarify the correlation between temperature and the COVID-19 pandemic in Hubei.Methods: We collected daily newly confirmed COVID-19 cases and daily temperature for six cities in Hubei Province, assessed their correlations, and established regression models.Results: For temperatures ranging from −3.9 to 16.5°C, daily newly confirmed cases were positively correlated with the maximum temperature ~0–4 days prior or the minimum temperature ~11–14 days prior to the diagnosis in almost all selected cities. An increase in the maximum temperature 4 days prior by 1°C was associated with an increase in the daily newly confirmed cases (~129) in Wuhan. The influence of temperature on the daily newly confirmed cases in Wuhan was much more significant than in other cities.Conclusion: Government departments in areas where temperatures range between −3.9 and 16.5°C and rise gradually must take more active measures to address the COVID-19 pandemic.

scholarly journals Analysis of Climate Change and Sustainable Development in Peru and Its Departments during the Year 2006 - 2018

2021 ◽  
pp. 31-46
Author(s):  
Vladimir Villarroel Diaz ◽  
Ronald Révolo Acevedo ◽  
Uriel Quispe Quezada ◽  
Elvis Carmen Delgadillo ◽  
Joel Colonio Llacua ◽  
...  
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
Minimum Temperature ◽  
Regression Models ◽  
Environmental Data ◽  
Maximum Temperature ◽  
Climate Index ◽  
P Value ◽  
Climatic Data ◽  
General Index

Aims: Analyze and relate the general index of climate change and sustainable development of Peru and its departments during the year 2006 - 2018. Study Design:  The research is not intended to deliberately manipulate the variables, therefore, it is non-experimental; is descriptive, correlational and longitudinal. Place and Duration of Study: The research project was carried out in the Faculty of Forestry and Environmental Sciences of the UNCP, likewise the collection of information data was carried out during 2020 and 2021, due to the Covid19 pandemic. Methodology: Two economic data, four social data and five environmental data were selected, in addition climatic data of precipitation, maximum and minimum temperature of the 24 departments of Peru were collected during the years 2006 - 2018; To estimate the climatic and sustainable indices, the Prescott-Allen methodology was applied, the interpretation and assessment scale (climate change and sustainable development) was carried out using the barometric analysis of McCarthy. Five regression models were applied [dependent variable GISD; independent variable IGCC], hypothesis testing was performed using Karl Pearson's r coefficient and p-value at 0.05. Results: It is stated that Peru presents an economic sustainable index [EcSI] of 0.066 low, social sustainability [SoSI]: 0.225 medium, environmental sustainability [EnSI]: 0.282 high and general index of sustainable development [GISD] is 0.572 medium. In itself the climate index of precipitation is [CPrI]: 0.079 weak, the climate index maximum temperature [CTxI]: 0.251 severe, climate index minimum temperature [CTnI]: 0.138 weak and the general index of climate change [GICC] is 0.468 moderate. Two appropriate regression models [linear and exponential] were determined to estimate the GISD as a function of the GICC, CPrI, CTxI and CTnI. Conclusion: It was found that during the year 2006 to 2018 Peru presented a low economic, social medium, high environmental situation and therefore its sustainable development is in a medium situation; while precipitation is weak, severe maximum temperature, weak minimum temperature, and therefore, climate change has a moderate impact. Likewise, it is stated that there are two linear and exponential regression models to estimate the GISD based on the GICC, CPrI, CTxI and CTnI. It is recommended to collect more climatic data and economic indicators to be able to differentiate the economic and climatic situation that Peru and departments represent during its thirteen years of development.

scholarly journals Recent trends in meteorological parameters over Jammu and Kashmir

MAUSAM ◽  
2021 ◽  
Vol 61 (3) ◽  
pp. 369-382
Author(s):  
A. K. JASWAL ◽  
G. S. PRAKASA RAO
Keyword(s):  
Relative Humidity ◽  
Minimum Temperature ◽  
Meteorological Parameters ◽  
Cloud Amount ◽  
The State ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Jammu And Kashmir ◽  
Total Cloud Amount ◽  
Increasing Trends

Annual trends of meteorological parameters temperature, rainfall, relative humidity and clouds for ten stations in Jammu and Kashmir during the period 1976-2007 were studied. Trend analysis shows that temperatures are increasing over the state with significant increase in maximum temperature in the Kashmir region (+0.04 to                +  0.05° C/year) and minimum temperature in the Jammu region (+0.03 to + 0.08° C/year). The diurnal temperature range (DTR) is increasing over Kashmir region due to higher increasing trends in the maximum temperature while the strong increasing trends in the minimum temperature are contributing more towards the decrease in DTR over the Jammu region. Annual rainfall and rainy days trends are decreasing in both the regions of the state except at Jammu where rainfall trend is significantly increasing (+12.05 mm/year). Day-time relative humidity trends are mixed while total cloud amount trends are decreasing over Kashmir region and increasing over Jammu region. The effects of urbanization in the last two decades are more pronounced in Jammu region and this is strongly expressed in minimum temperature over the region. The warming trends observed over Jammu and Kashmir state during the period of study need further investigation in relation to variability of atmospheric circulation over North India.

scholarly journals Aphid incidence and its correlation with different environmental factors

1970 ◽  
Vol 7 (1) ◽  
pp. 15-18 ◽  
Author(s):  
MR Hasan ◽  
M Ahmad ◽  
MH Rahman ◽  
MA Haque
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Environmental Factors ◽  
Minimum Temperature ◽  
Dew Point ◽  
Aphid Population ◽  
Maximum Temperature ◽  
Mustard Aphid ◽  
Mustard Plant ◽  
Rain Fall

The aphid incidence and its correlation with environmental factors were studied. Mustard variety "Sampad" was used as test crop. Aphid incidence varied significantly at various parts of mustard plant and time of the day. The highest number of aphid was observed in the vegetative parts of the mustard plant in the morning. High cloudiness, relative humidity and dew point favoured the aphid population and slight rain fall quickly declined the aphid population. Among the different environmental factors maximum temperature, dew point and sun shine hours were positively correlated with aphid population and minimum temperature, relative humidity and wind speed were negatively correlated with aphid population. Keywords: Mustard aphid; Incidence; Environmental factors DOI: 10.3329/jbau.v7i1.4791 J. Bangladesh Agril. Univ. 7(1): 15-18, 2009

scholarly journals Network of Automatic Weather Stations : Pseudo random burst sequence type

MAUSAM ◽  
2021 ◽  
Vol 63 (4) ◽  
pp. 587-606
Author(s):  
M.R. RANALKAR ◽  
R.P. MISHRA ◽  
ANJIT ANJAN ◽  
S. KRISHNAIAH
Keyword(s):  
Soil Temperature ◽  
Real Time ◽  
Minimum Temperature ◽  
Dew Point ◽  
Maximum Temperature ◽  
Earth Station ◽  
Level Pressure ◽  
Station Data ◽  
Hourly Rainfall ◽  
Weather Stations

A network of 125 Automatic Weather Stations (AWS) has been set up by India Meteorological Department (IMD) during the year 2006-07 across India. Each station is configured to measure air temperature, hourly maximum temperature, hourly minimum temperature, relative humidity, station level pressure, hourly rainfall and cumulative rainfall for the day, Wind speed and Wind direction. In addition to these parameters, 25 stations provide data for global solar radiation and soil temperature. Five stations also provide soil moisture in addition to soil temperature. Each station transmits a data stream at an interval of an hour in a Pseudo Random Burst Sequence (PRBS) manner via UHF transmitter and a dedicated meteorological satellite KALPANA-1/ INSAT-3A to the central AWS data receiving Earth Station facility established at IMD, Pune. Mean sea level pressure, dew point temperature, duration of bright sunshine and daily maximum & minimum temperature are derived at the receiving Earth Station. Data archival in near real time is done at the receiving Earth Station. Data dissemination in WMO code form is also done in near real time through Global Telecommunication System. This paper provides technical description of various sub-systems of PRBS type Indian Automatic Weather Station network including instrument, satellite transmission technique, sensor characteristics, siting and exposure conditions and performance of a representative station.

scholarly journals The Use of Multiple Linear Regression Model for Predicting The Influence of Weather to The Prevalence of Newcastle Disease in Chicken in North Aceh

2008 ◽  
Vol 2 (1) ◽  
Author(s):  
Mahdi Abrar
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Newcastle Disease ◽  
Linear Regression Model ◽  
Minimum Temperature ◽  
Multiple Linear Regression Model ◽  
Maximum Temperature ◽  
Independent Variables ◽  
Dependent Variables

The objective of this research is to see the influence of weather on the prevalence of Newcastle Disease (ND) in chicken in Kabupaten Aceh Utara (North Aceh). Data used in this research were obtained from Dinas Peternakan North Aceh for the number of chicken suffered ND and from Badan Meteorologi dan Geofisika Lhokseumawe, North Aceh for the form of weather. Multiple Linear Regression Model with five independent variables (the average of rainfall per month, the average of maximum temperature, the average of minimum temperature, the velocity of the wind, and the average of humidity per month) was used to see the influence of wheather to the prevalence of Newcastle Disease. Proportion the number of chicken suffered from ND which is the ratio of the number of chicken suffered from ND to the total number of chicken was used as dependent variables. The result shows that the best model is Ŷ= 120.529278 – 1.33 x wind humidity + 1.907 x wind velocity.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China

2021 ◽  
Vol 13 (5) ◽  
pp. 913
Author(s):  
Hua Liu ◽  
Xuejian Li ◽  
Fangjie Mao ◽  
Meng Zhang ◽  
Di’en Zhu ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Distribution Area ◽  
Spatiotemporal Pattern ◽  
Climate Factors ◽  
Important Indicator ◽  
Fractional Vegetation Cover ◽  
Annual Minimum Temperature ◽  
Total Study Area

The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC.

scholarly journals Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peixin Ren ◽  
Zelin Liu ◽  
Xiaolu Zhou ◽  
Changhui Peng ◽  
Jingfeng Xiao ◽  
...  
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Time Lag ◽  
Evergreen Forest ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Study Region ◽  
Cumulative Precipitation

Abstract Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

scholarly journals The effect of average temperature on suicide rates in five urban California counties, 1999–⁠2019: an ecological time series analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sierra Cheng ◽  
Rebecca Plouffe ◽  
Stephanie M. Nanos ◽  
Mavra Qamar ◽  
David N. Fisman ◽  
...  
Keyword(s):  
Public Health ◽  
Suicide Rate ◽  
Minimum Temperature ◽  
The United States ◽  
Additive Models ◽  
Maximum Temperature ◽  
Mortality Data ◽  
Average Temperature ◽  
Suicide Rates ◽  
Average Maximum

Abstract Background Suicide is among the top 10 leading causes of premature morality in the United States and its rates continue to increase. Thus, its prevention has become a salient public health responsibility. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. The purpose of the present study is to evaluate the association between average temperature and suicide rates in the five most populous counties in California using mortality data from 1999 to 2019. Methods Monthly counts of death by suicide for the five counties of interest were obtained from CDC WONDER. Monthly average, maximum, and minimum temperature were obtained from nCLIMDIV for the same time period. We modelled the association of each temperature variable with suicide rate using negative binomial generalized additive models accounting for the county-specific annual trend and monthly seasonality. Results There were over 38,000 deaths by suicide in California’s five most populous counties between 1999 and 2019. An increase in average temperature of 1 °C corresponded to a 0.82% increase in suicide rate (IRR = 1.0082 per °C; 95% CI = 1.0025–1.0140). Estimated coefficients for maximum temperature (IRR = 1.0069 per °C; 95% CI = 1.0021–1.0117) and minimum temperature (IRR = 1.0088 per °C; 95% CI = 1.0023–1.0153) were similar. Conclusion This study adds to a growing body of evidence supporting a causal effect of elevated temperature on suicide. Further investigation into environmental causes of suicide, as well as the biological and societal contexts mediating these relationships, is critical for the development and implementation of new public health interventions to reduce the incidence of suicide, particularly in the face increasing temperatures due to climate change.

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